Mitigating Spurious Correlation with Sample Clusterness
Published:Dec 28, 2025 10:54
•1 min read
•ArXiv
Analysis
This paper addresses the problem of spurious correlations in deep learning models, a significant issue that can lead to poor generalization. The proposed data-oriented approach, which leverages the 'clusterness' of samples influenced by spurious features, offers a novel perspective. The pipeline of identifying, neutralizing, eliminating, and updating is well-defined and provides a clear methodology. The reported improvement in worst group accuracy (over 20%) compared to ERM is a strong indicator of the method's effectiveness. The availability of code and checkpoints enhances reproducibility and practical application.
Key Takeaways
- •Proposes a data-oriented approach to mitigate spurious correlations.
- •Leverages the 'clusterness' of samples to identify and neutralize spurious features.
- •Achieves significant improvement in worst group accuracy compared to ERM.
- •Provides code and checkpoints for reproducibility.
Reference
“Samples influenced by spurious features tend to exhibit a dispersed distribution in the learned feature space.”